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<p>Artificial Intelligence (AI) is applied in this research for the analysis of a novel green method for production of nanomedicine. The method is based on supercritical solvent for production of drug nanoparticles in which the AI was used to estimate the solubility of drug in the supercritical solvent. Carbon dioxide was considered as the supercritical solvent in this study and the effect of pressure and temperature on the drug solubility was evaluated using the developed AI-based models. The aim here is to model and analyze the solubility of Clobetasol Propionate (CP) based on two key parameters: temperature and pressure. Ensemble models based on decision tree are selected to make models. The models include gradient boosting (GBDT), extremely randomized trees (ET), and random forest (RF) and tuned using ant colony optimization (ACO). Final models have acceptable results, all with R<sup>2</sup> criterion more than 0.9. The GBDT model outperforms the others with an R<sup>2</sup> of 0.987. Additionally, the RMSE for this model is minimized to 8.21 × 10<sup>–3</sup>.</p>

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Computational intelligence modeling and optimization of small molecule API solubility in supercritical solvent for production of drug nanoparticles

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月28日

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  • Jiaxin Liang,
  • Hai Zhao,
  • Shengxue Zhou,
  • Yang Gao

摘要

Artificial Intelligence (AI) is applied in this research for the analysis of a novel green method for production of nanomedicine. The method is based on supercritical solvent for production of drug nanoparticles in which the AI was used to estimate the solubility of drug in the supercritical solvent. Carbon dioxide was considered as the supercritical solvent in this study and the effect of pressure and temperature on the drug solubility was evaluated using the developed AI-based models. The aim here is to model and analyze the solubility of Clobetasol Propionate (CP) based on two key parameters: temperature and pressure. Ensemble models based on decision tree are selected to make models. The models include gradient boosting (GBDT), extremely randomized trees (ET), and random forest (RF) and tuned using ant colony optimization (ACO). Final models have acceptable results, all with R2 criterion more than 0.9. The GBDT model outperforms the others with an R2 of 0.987. Additionally, the RMSE for this model is minimized to 8.21 × 10–3.